A pickling process section hydrogen concentration online monitoring and early warning method and system

Abstract

The present disclosure belongs to the technical field of cold rolling treatment, and specifically provides an acid process section hydrogen concentration online monitoring and early warning method and system, wherein the method comprises: obtaining comprehensive data of acid mist hydrogen concentration, acid liquid temperature and acid liquid concentration from an acid pickling process section controller through a unit level Ethernet; processing the comprehensive data to form sample data and obtaining a hydrogen concentration prediction model after training; inputting real-time data of the acid mist hydrogen concentration, the acid liquid temperature and the acid liquid concentration into the hydrogen concentration prediction model, outputting predicted acid mist hydrogen concentration in a future time window; and obtaining future warning suggestions according to a warning level and the predicted acid mist hydrogen concentration. The present disclosure adopts all-around online acquisition of key acid pickling process parameters, is good in visualization, and can timely remind in an abnormal situation; the present disclosure predicts the acid mist hydrogen concentration and temperature in a future time window and gives corresponding suggestions, thereby avoiding hydrogen explosion accidents caused by hydrogen gathering to a certain concentration and encountering fire.

Description

Technical Field

[0001] This disclosure relates to the field of cold rolling processing technology, and in particular to a method and system for online monitoring and early warning of hydrogen concentration in the pickling process. Background Technology

[0002] The pickling process mainly includes a pickling tank and a rinsing tank. In the pickling tank, the acid reacts chemically with the iron oxide scale on the surface of the strip steel to remove the iron oxide scale. The rinsing tank cleans the surface of the strip steel that has come out of the pickling tank to remove any remaining pickling residue.

[0003] During the pickling process, achieving the perfect pickling temperature—neither under-pickling nor over-pickling—is challenging. To avoid under-pickling, the acid temperature and concentration are typically increased. Under these conditions, the strip steel is over-pickled, causing the acid to react with the strip substrate and produce hydrogen gas. If the hydrogen concentration accumulates to a certain level, it can ignite and cause a hydrogen explosion, damaging equipment, causing unit shutdowns, and resulting in significant losses. Summary of the Invention

[0004] This disclosure aims to solve at least one of the technical problems existing in the prior art, and proposes a method and system for online monitoring and early warning of hydrogen concentration in the pickling process.

[0005] Firstly, this disclosure provides a method for online monitoring and early warning of hydrogen concentration in an acid process section, including:

[0006] Comprehensive data on acid mist hydrogen concentration, acid temperature, and acid concentration are obtained from the pickling process section controller via the unit's primary Ethernet.

[0007] The comprehensive data is processed to form sample data, and then trained to obtain a hydrogen concentration prediction model.

[0008] The real-time data of acid mist hydrogen concentration, acid liquid temperature and acid liquid concentration are input into the hydrogen concentration prediction model, and the predicted acid mist hydrogen concentration within a certain time window is output.

[0009] Based on the warning level and the predicted acid mist hydrogen concentration, future warning suggestions are obtained.

[0010] Preferably, the step of acquiring comprehensive data on acid mist hydrogen concentration, acid temperature, and acid concentration from the pickling process section controller via the unit's primary Ethernet connection specifically includes:

[0011] The hydrogen concentration data acquisition and visualization module obtains comprehensive data on acid mist hydrogen concentration, acid temperature, and acid concentration from the acid washing process section controller via the unit's primary Ethernet.

[0012] The comprehensive data is stored in the hydrogen concentration online early warning server and then visualized on the server's monitor.

[0013] Preferably, the step of processing the comprehensive data to form sample data and training it to obtain the hydrogen concentration prediction model specifically includes:

[0014] The data on acid mist hydrogen concentration, acid liquid temperature, and acid liquid concentration were processed to form sample data;

[0015] The sample data is divided into training data, validation data, and test dataset;

[0016] Based on the sample data, DeepAR, Informer, LSTNet, MLP, NBEATS, NHiTS, RNN, SCINet, TCN, TFT, or Transformer models were selected for training and validation until the prediction deviation of the acid mist hydrogen concentration model was controlled within the allowable range.

[0017] Preferably, the acid mist hydrogen concentration is the target to be predicted, and the strip grade, strip width, strip thickness, strip speed, acid temperature, acid concentration, and time are covariates.

[0018] Preferably, the ratio of the training data, validation data, and test dataset is 7:2:1.

[0019] Preferably, the step of selecting the LSTNet model for training and validation based on sample data specifically includes:

[0020] First, PaddleTS is used to build the model network, and the time series length of the model input, the time series length of the model output, the loss function, the optimization algorithm, the optimizer parameters, and the maximum number of training rounds are predefined.

[0021] Use lstm.fit(train_dataset,val_dataset) to train and validate the model using sample data, where train_dataset is the training dataset and val_dataset is the validation dataset;

[0022] During and after training, MAE (Mean Absolute Error) and MSE (Mean Squared Error) are used to evaluate the model's prediction performance. When the performance reaches the preset value, the hydrogen concentration prediction model is obtained.

[0023] Preferably, after the training process is completed, the trained hydrogen concentration prediction model is saved using LSTM.

[0024] Preferably, the warning levels include: recommending to pay attention to operation, recommending to conduct necessary inspections at an appropriate time, recommending to plan to shut down for maintenance in the near future, and recommending to take maintenance measures as soon as possible.

[0025] Secondly, this disclosure provides an online monitoring and early warning system for hydrogen concentration in the pickling process section. The system can be used to implement an online monitoring and early warning method for hydrogen concentration in the pickling process section. The system includes:

[0026] The data acquisition module is configured to acquire comprehensive data on acid mist hydrogen concentration, acid temperature, and acid concentration from the pickling process section controller via the unit's primary Ethernet.

[0027] The model training module is configured to process the comprehensive data to form sample data and then train the model to obtain a hydrogen concentration prediction model.

[0028] The prediction module is configured to input real-time data of acid mist hydrogen concentration, acid liquid temperature and acid liquid concentration into the hydrogen concentration prediction model, and output the predicted acid mist hydrogen concentration within a certain time window in the future.

[0029] The early warning module is configured to generate future early warning suggestions based on the early warning level and the predicted acid mist hydrogen concentration.

[0030] Thirdly, this disclosure provides an electronic device, including:

[0031] One or more processors;

[0032] Memory, used to store one or more programs;

[0033] When the one or more programs are executed by the one or more processors, the one or more processors implement a method for online monitoring and early warning of hydrogen concentration in the acid washing process section. Attached Figure Description

[0034] Figure 1 A flowchart illustrating an online monitoring and early warning method for hydrogen concentration in an acid washing process, provided in this embodiment of the present disclosure;

[0035] Figure 2 A schematic diagram of an online monitoring and early warning system for hydrogen concentration in an acid washing process section provided in this embodiment of the present disclosure;

[0036] Figure 3 This is a flowchart of the hydrogen concentration prediction training module provided in this publication;

[0037] Figure 4 This is a structural block diagram of an electronic device provided in an embodiment of the present disclosure. Detailed Implementation

[0038] To enable those skilled in the art to better understand the technical solutions of this disclosure, the disclosure will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0039] Unless otherwise defined, the technical or scientific terms used in this disclosure shall have the ordinary meaning understood by one of ordinary skill in the art to which this disclosure pertains. The terms “first,” “second,” and similar terms used in this disclosure are not intended to indicate any order, quantity, or importance, but are merely used to distinguish different components. Similarly, the terms “an,” “a,” or “the,” and similar terms are not intended to limit the quantity, but rather to indicate the presence of at least one. The terms “comprising,” “including,” or “including,” and similar terms mean that the element or object preceding the term encompasses the elements or objects listed following the term and their equivalents, without excluding other elements or objects. The terms “connected,” “linked,” and similar terms are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. The terms “upper,” “lower,” “left,” and “right,” etc., are used only for relative positional relationships, and these relative positional relationships may change accordingly when the absolute position of the described object changes.

[0040] In the various figures, the same elements are represented by similar reference numerals. For clarity, not all parts in the figures are drawn to scale. Furthermore, some well-known parts may not be shown in the figures.

[0041] Many specific details of this disclosure, such as the structure, materials, dimensions, processing methods, and techniques of the components, are described below to provide a clearer understanding of the disclosure. However, as those skilled in the art will understand, this disclosure may be implemented without following these specific details.

[0042] like Figure 1 and Figure 2 As shown in the embodiments of this disclosure, an online monitoring and early warning method for hydrogen concentration in an acid process section is provided, including the following steps:

[0043] S1 obtains comprehensive data on acid mist hydrogen concentration, acid temperature, and acid concentration from the pickling process section controller via the unit's primary Ethernet.

[0044] S2, after processing the comprehensive data to form sample data and training it, a hydrogen concentration prediction model is obtained;

[0045] S3, input the real-time data of acid mist hydrogen concentration, acid liquid temperature and acid liquid concentration into the hydrogen concentration prediction model, and output the predicted acid mist hydrogen concentration within a certain time window in the future.

[0046] S4. Based on the warning level and the predicted acid mist hydrogen concentration, future warning suggestions are obtained.

[0047] An online monitoring and early warning system for hydrogen concentration in the pickling process section was established. The system comprises a strip steel unit (C1), an acid tank (C2), an acid solution unit (C3), an acid mist unit (C4), online hydrogen concentration monitoring instruments (C5, C6, C7), an online acid solution temperature monitoring instrument (C7), a pickling process section controller (C8), a unit-level Ethernet network (C9), a hydrogen concentration online early warning server (C10), and a hydrogen concentration online early warning module (C11). The online hydrogen concentration monitoring instrument (C5) detects the hydrogen concentration in the acid mist and transmits the data to the pickling process section controller (C8). The online acid solution temperature monitoring instrument (C6) detects the acid solution temperature and transmits the data to the pickling process section controller (C8). The online acid solution concentration monitoring instrument (C7) detects the acid solution concentration and transmits the data to the pickling process section controller (C8). The hydrogen concentration online early warning server (C10) obtains data on the acid mist hydrogen concentration, acid solution temperature, and acid solution concentration from the pickling process section controller (C8) via the unit-level Ethernet network (C9). The hydrogen concentration online early warning module (C11) runs on the hydrogen concentration online early warning server (C10), monitoring the hydrogen concentration and issuing early warnings.

[0048] The hydrogen concentration online early warning module C11 mainly consists of a hydrogen concentration data acquisition and visualization module, a hydrogen concentration prediction and training module, and a hydrogen concentration online early warning module.

[0049] During production, a hydrogen concentration data acquisition and visualization module is activated. This module acquires comprehensive data on acid mist hydrogen concentration, acid solution temperature, and acid solution concentration from the pickling process section controller C8 via the unit's primary Ethernet port C9. This comprehensive data is then stored on the hard drive of the hydrogen concentration online early warning server C10 and displayed visually on the server's monitor. The displayed information includes: acid tank number, acid mist hydrogen concentration, acid solution temperature, real-time acid solution concentration parameters, and operational statistics.

[0050] like Figure 3 As shown, the hydrogen concentration prediction training module is run to process the data of acid mist hydrogen concentration, acid liquid temperature and acid liquid concentration to form sample data. The acid mist hydrogen concentration is the target to be predicted, and the strip steel grade, strip steel width, strip steel thickness, strip steel speed, acid liquid temperature, acid liquid concentration and time are covariates.

[0051] The sample data was then divided into training, validation, and test datasets, with 70% of the samples used for training, 20% for validation, and 10% for testing. Based on the sample data, DeepAR, Informer, LSTNet, MLP, NBEATS, NHiTS, RNN, SCINet, TCN, TFT, or Transformer models were selected for training and validation until the prediction bias of the acid mist hydrogen concentration model was controlled within the allowable range.

[0052] Taking LSTNet as an example:

[0053] First, PaddleTS (a Python library for time series modeling based on Baidu's deep learning framework PaddlePaddle) is used to build the model network. This mainly involves predefining parameters such as in_chuck_len (length of the time series input to the model), out_chunk_len (length of the time series output to the model), loss_fn (loss function), optimizer_fn (optimization algorithm), optimizer_paras (optimizer parameters), and max_epochs (maximum number of training epochs). After the model parameters are predefined, lstm.fit(train_dataset, val_dataset) is used to train and validate the model using the dataset, where train_dataset is the training dataset and val_dataset is the validation dataset. During and after training, MAE (Mean Absolute Error) and MSE (Mean Squared Error) are used to evaluate the prediction performance of the hydrogen concentration prediction model. The smaller the MAE and MSE, the better the training effect. After training, a hydrogen concentration prediction model is obtained. The trained model is saved using lstm(lstm.save)("lstm"), which can then be called by the online hydrogen concentration early warning module to predict the acid mist hydrogen concentration within a certain time window in the future.

[0054] The C11 online hydrogen concentration early warning module collects real-time data on acid mist hydrogen concentration, acid liquid temperature, and acid liquid concentration. When these data exceed thresholds, an alarm is triggered. Simultaneously, a trained acid mist hydrogen concentration prediction model is used to predict the acid mist hydrogen concentration within a certain future time window. The early warning system has four levels: "Recommend monitoring," "Recommend necessary checks at an appropriate time," "Recommend planned shutdown for maintenance," and "Recommend immediate maintenance." This prevents hydrogen from accumulating to a certain concentration and igniting, potentially causing a hydrogen explosion.

[0055] The purpose of this disclosure is to provide an online monitoring and early warning system for hydrogen concentration in the pickling process section, which continuously monitors the hydrogen concentration in the pickling process section and issues early warnings under the condition of full-speed operation of the production line, so as to avoid hydrogen explosion accidents caused by hydrogen accumulating to a certain concentration and then encountering fire.

[0056] Beneficial effects:

[0057] The acid mist concentration, acid temperature, and acid concentration data are detected by online hydrogen concentration detection instruments, online acid temperature detection instruments, and online acid concentration detection instruments, providing comprehensive online acquisition of key acid washing process parameters with good visualization.

[0058] The online hydrogen concentration early warning module collects real-time data on acid mist hydrogen concentration, acid liquid temperature, and acid liquid concentration. When these data exceed the threshold, an alarm is triggered to provide timely reminders.

[0059] The online hydrogen concentration early warning module predicts the concentration and temperature of acid mist hydrogen within a certain time window. The early warning system is divided into four levels: "Recommend monitoring," "Recommend necessary inspections at an appropriate time," "Recommend planned shutdown for maintenance," and "Recommend immediate maintenance measures." This aims to prevent hydrogen explosion accidents caused by hydrogen accumulating to a certain concentration and then igniting.

[0060] This disclosure also provides an online monitoring and early warning system for hydrogen concentration in the pickling process section. The system can be used to implement the above-mentioned online monitoring and early warning method for hydrogen concentration in the pickling process section. The system includes:

[0061] The data acquisition module is configured to acquire comprehensive data on acid mist hydrogen concentration, acid temperature, and acid concentration from the pickling process section controller via the unit's primary Ethernet.

[0062] The model training module is configured to process the comprehensive data to form sample data and then train the model to obtain a hydrogen concentration prediction model.

[0063] The prediction module is configured to input real-time data of acid mist hydrogen concentration, acid liquid temperature and acid liquid concentration into the hydrogen concentration prediction model, and output the predicted acid mist hydrogen concentration within a certain time window in the future.

[0064] The early warning module is configured to generate future early warning suggestions based on the early warning level and the predicted acid mist hydrogen concentration.

[0065] Please see Figure 4 This is a schematic diagram illustrating an embodiment of the electronic device provided in this invention. For example... Figure 4 As shown, an embodiment of the present invention provides an electronic device 1300, including a memory 1310, a processor 1320, and a computer program 1311 stored in the memory 1310 and executable on the processor 1320. When the processor 1320 executes the computer program 1311, it performs the following steps: S1, obtaining comprehensive data of acid mist hydrogen concentration, acid temperature, and acid concentration from the pickling process section controller via the unit's first-level Ethernet.

[0066] S2, after processing the comprehensive data to form sample data and training it, a hydrogen concentration prediction model is obtained;

[0067] S3, input the real-time data of acid mist hydrogen concentration, acid liquid temperature and acid liquid concentration into the hydrogen concentration prediction model, and output the predicted acid mist hydrogen concentration within a certain time window in the future.

[0068] S4. Based on the warning level and the predicted acid mist hydrogen concentration, future warning suggestions are obtained.

[0069] It should be noted that the computer-readable medium disclosed herein may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium may be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0070] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0071] It is understood that the above embodiments are merely exemplary implementations used to illustrate the principles of the present invention, and the present invention is not limited thereto. For those skilled in the art, various modifications and improvements can be made without departing from the spirit and essence of the present invention, and these modifications and improvements are also considered to be within the scope of protection of the present invention.

Claims

1. A method for online monitoring and early warning of hydrogen concentration in an acid washing process, characterized in that, include: Comprehensive data on acid mist hydrogen concentration, acid temperature, and acid concentration are obtained from the pickling process section controller via the unit's primary Ethernet. The comprehensive data is processed to form sample data, and then trained to obtain a hydrogen concentration prediction model. The real-time data of acid mist hydrogen concentration, acid liquid temperature and acid liquid concentration are input into the hydrogen concentration prediction model, and the predicted acid mist hydrogen concentration within a certain time window is output. Based on the warning level and the predicted acid mist hydrogen concentration, future warning suggestions are obtained; The process of processing comprehensive data to form sample data and training it to obtain a hydrogen concentration prediction model specifically includes: The data on acid mist hydrogen concentration, acid liquid temperature, and acid liquid concentration were processed to form sample data; The sample data is divided into training data, validation data, and test dataset; Based on the sample data, DeepAR, Informer, LSTNet, MLP, NBEATS, NHiTS, RNN, SCINet, TCN, TFT, or Transformer models were selected for training and validation until the prediction deviation of the acid mist hydrogen concentration model was controlled within the allowable range. The acid mist hydrogen concentration is the target to be predicted, and the strip steel grade, strip steel width, strip steel thickness, strip steel speed, acid liquid temperature, acid liquid concentration and time are covariates; The ratio of the training data, validation data, and test dataset is 7:2:

1.

2. The method for online monitoring and early warning of hydrogen concentration in the pickling process section according to claim 1, characterized in that, The acquisition of comprehensive data on acid mist hydrogen concentration, acid temperature, and acid concentration from the pickling process section controller via the unit's primary Ethernet connection specifically includes: The hydrogen concentration data acquisition and visualization module obtains comprehensive data on acid mist hydrogen concentration, acid temperature, and acid concentration from the acid washing process section controller via the unit's primary Ethernet. The comprehensive data is stored in the hydrogen concentration online early warning server and then visualized on the server's monitor.

3. The method for online monitoring and early warning of hydrogen concentration in the pickling process section according to claim 1, characterized in that, The selection of the LSTNet model for training and validation based on sample data specifically includes: First, PaddleTS is used to build the model network, and the time series length of the model input, the time series length of the model output, the loss function, the optimization algorithm, the optimizer parameters, and the maximum number of training rounds are predefined. Use lstm.fit(train_dataset, val_dataset) to train and validate the model on sample data, where train_dataset is the training dataset and val_dataset is the validation dataset; During and after training, MAE (Mean Absolute Error) and MSE (Mean Squared Error) are used to evaluate the model's prediction performance. When the performance reaches the preset value, the hydrogen concentration prediction model is obtained.

4. The method for online monitoring and early warning of hydrogen concentration in the pickling process section according to claim 3, characterized in that, After the training process is completed, the trained hydrogen concentration prediction model is saved using LSTM.

5. The method for online monitoring and early warning of hydrogen concentration in the pickling process section according to claim 1, characterized in that, The warning levels include: it is recommended to pay attention to the operation, it is recommended to conduct necessary inspections at an appropriate time, it is recommended to plan to shut down for maintenance in the near future, and it is recommended to take maintenance measures as soon as possible.

6. An online monitoring and early warning system for hydrogen concentration in an acid pickling process, characterized in that, The system can be used to implement the online monitoring and early warning method for hydrogen concentration in the pickling process section as described in any of claims 1 to 5. The system includes: The data acquisition module is configured to acquire comprehensive data on acid mist hydrogen concentration, acid temperature, and acid concentration from the pickling process section controller via the unit's primary Ethernet. The model training module is configured to process the comprehensive data to form sample data and then train the model to obtain a hydrogen concentration prediction model. The prediction module is configured to input real-time data of acid mist hydrogen concentration, acid liquid temperature and acid liquid concentration into the hydrogen concentration prediction model, and output the predicted acid mist hydrogen concentration within a certain time window in the future. The early warning module is configured to generate future early warning suggestions based on the early warning level and the predicted acid mist hydrogen concentration.

7. An electronic device, characterized in that, include: One or more processors; Memory, used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the online monitoring and early warning method for hydrogen concentration in the pickling process section as described in any one of claims 1 to 5.

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